Characterization of relationship between tau pathology and neurodegeneration in Alzheimer's disease using multimodal imaging

Project Summary The pathophysiology of Alzheimer’s disease (AD) is characterized by the accumulation of Amyloid (Aβ) plaques and tau neurofibrillary tangles (NFT). While the presence of both plaques (A+) and tangles (T+) are essential to the biological definition of AD as recently codified in the ATN research classification framework, tau (T) is thought to be the primary driver of downstream neurodegeneration (N) and the resulting cognitive impairment. However, there is substantial variability in the T-N relationship – manifested in higher or lower atrophy than expected for the level of tau in a given brain region, even in carefully curated research cohorts. What does this variability represent? In this study, we explore the idea that a quantitative measure of the variability in the canonical relationship between T and N is itself a “mismatch metric” that can help characterize different underlying phenotypes and modulatory factors. We will examine this by modeling region-wise measures of T vs. N obtained from in-vivo imaging in a cohort A+ symptomatic individuals. SUVR from tau-PET imaging and cortical thickness from structural MRI will serve as regional measures of T and N respectively. We will then use data-driven clustering for phenotype discovery based on the model residuals. Region-wise model residuals capture spatial variation in the T-N relationship, conceptually extending the ATN framework from the dichotomous T/N +/- designations to a richer description that may reflect differing spatial topography of underlying co-pathologies. The concept of the T-N mismatch metric and its ability to identify underlying phenotypes will be evaluated in multiple publicly available and institutional datasets, each of which will provide a diverse collection of phenotypes. We will also perform evaluation in a dataset of ex-vivo specimens of A+ individuals. We will obtain quantitative measures of N from ex-vivo MRI as a semi-automated cortical thickness estimate, and of T using digital histopathology techniques, in multiple brain regions. Gold standard histopathology measures (e.g. TDP- 43, alpha-synuclein, non-AD tau, vascular disease) obtained in these samples will help evaluate whether T-N mismatch metric can help identify phenotypes with non-AD co-pathology. Finally, we will evaluate if the T-N mismatch metric is predictive of future cognitive decline as well as rates of longitudinal neurodegenerative changes in the brain.